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Strategies for high-performance resource-efficient compression of neural spike recordings.

Thorbergsson, Palmi Thor LU ; Garwicz, Martin LU ; Schouenborg, Jens LU and Johansson, Anders J LU (2014) In PLoS ONE 9(4).
Abstract
Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve... (More)
Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
9
issue
4
publisher
Public Library of Science
external identifiers
  • pmid:24727834
  • wos:000336736200026
  • scopus:84899629257
ISSN
1932-6203
DOI
10.1371/journal.pone.0093779
language
English
LU publication?
yes
id
2e48298b-c20b-405c-9ad1-42cf559f99c1 (old id 4430188)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/24727834?dopt=Abstract
date added to LUP
2014-05-05 20:26:30
date last changed
2017-08-13 03:54:46
@article{2e48298b-c20b-405c-9ad1-42cf559f99c1,
  abstract     = {Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used.},
  articleno    = {e93779},
  author       = {Thorbergsson, Palmi Thor and Garwicz, Martin and Schouenborg, Jens and Johansson, Anders J},
  issn         = {1932-6203},
  language     = {eng},
  number       = {4},
  publisher    = {Public Library of Science},
  series       = {PLoS ONE},
  title        = {Strategies for high-performance resource-efficient compression of neural spike recordings.},
  url          = {http://dx.doi.org/10.1371/journal.pone.0093779},
  volume       = {9},
  year         = {2014},
}